Mobile manipulation in cluttered environments with humanoids: Integrated perception, task planning, and action execution

Hornung, Armin; Böttcher, Sebastian; Schlagenhauf, Jonas; Dornhege, Christian; Hertle, Andreas; Bennewitz, Maren

doi:10.1109/humanoids.2014.7041451

Cited by 23 publications

(15 citation statements)

References 10 publications

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“…Therefore, tasks are split-up into subtasks and the first subtask is planned and executed before the subsequent one. Planning time increases exponentially as the number of objects increases [45,46]; thus, to keep planning times tractable, only state relevant to the robot’s current context is inserted into the planning problems. In our coffee bot scenario, these features enabled the robot to start acting after 3.68 s rather than 11.30 s. Conversely, planning everything upfront guarantees completeness and, when no replanning is required, the plan is likely to be more optimal than when a hierarchy is used.…”

Section: Discussionmentioning

confidence: 99%

“…When the number of objects within a problem file is increased, an exponential increase in the planning time is observed. This is due to the state explosion problem [45,46]. The fewer objects included within the PDDL problem, the fewer possible states and therefore state transitions, the planner has to search through.…”

Section: Designing the Hierarchy For Smart Environmentsmentioning

confidence: 99%

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Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

Harman

Chintamani

Simoens

2019

Sensors

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By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner increases the computational load, and thus can delay the execution of a task. Moreover, tasks may be frequently replanned due to the unanticipated actions of humans. Our framework aims to mitigate these inadequacies. In this paper, we propose a continual planning framework, which incorporates the sensing and actuation capabilities of IoT devices into a robot’s state estimation, task planing and task execution. The robot’s onboard task planner queries a cloud-based framework for actuators, capable of the actions the robot cannot execute. Once generated, the plan is sent to the cloud back-end, which will inform the robot if any IoT device reports a state change affecting its plan. Moreover, a Hierarchical Continual Planning in the Now approach was developed in which tasks are split-up into subtasks. To delay the planning of actions that will not be promptly executed, and thus to reduce the frequency of replanning, the first subtask is planned and executed before the subsequent subtask is. Only information relevant to the current (sub)task is provided to the task planner. We apply our framework to a smart home and office scenario in which the robot is tasked with carrying out a human’s requests. A prototype implementation in a smart home, and simulator-based evaluation results, are presented to demonstrate the effectiveness of our framework.

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Section: Discussionmentioning

confidence: 99%

Section: Designing the Hierarchy For Smart Environmentsmentioning

confidence: 99%

Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

Harman

Chintamani

Simoens

2019

Sensors

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“…For example, (semi) humanoid robots have shown a good level of proficiency in performing manipulative tasks involving direct handling of objects within harsh environments. These tasks may involve the retrieval of samples, opening doors, or moving obstacles in cluttered environments (Hornung et al, 2014). Latest research have also begun to explore the use of various locomotion mechanisms as base for the humanoid robot to improve locomotion performance in challenging terrain (Badger et al, 2016;Khatib et al, 2016;Yaguchi et al, 2015).…”

Section: Research Trends and Directionsmentioning

confidence: 99%

Autonomous robots for harsh environments: a holistic overview of current solutions and ongoing challenges

Wong

Yang

Yan

et al. 2018

Systems Science & Control Engineering

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This paper presents a holistic overview of robotics and autonomous systems developed for applications in harsh environments where interactions with robots are difficult to model due to reasons such as dynamics, uncertainty, complexity and unpredictability. Robots of different characteristics and designs are required to perform different tasks within these environments. A number of classes of robots emerge as particularly favoured by the research community for solving challenging problems in difficult environments. Key examples of these deployments are studied with an analysis on how high-level autonomy is a key issue that must be addressed. The surveyed work suggest that the lack of observable autonomy in many systems are a consequence of both the complexity of the problem and the lack of proven reliability of autonomous solutions for applications that demand high success rates. This paper provides a broad and general overview of autonomous robots deployed in harsh environments and the commonalities of the challenges and existing solutions across application areas, including oil and gas inspection, space exploration, deep-sea operations and search and rescue. ARTICLE HISTORY

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“…Several recent works exemplifies planning in cluttered environments. [1][2][3][4][5] For the planning strategy in ref. [1], a skin type sensor may be needed to make it feasible for whole-body reactive planning in a cluttered environment.…”

Section: Introductionmentioning

confidence: 99%

“…[3] by approximating obstacles to convex shapes thereby no local minima exists. Other examples in this regard 4,5 demonstrate escalating interests in the area of manipulation in cluttered environments.…”

Section: Introductionmentioning

confidence: 99%

Safe manipulation in unknown, crowded environments via sensor-based interleaving planner: interleaving software and sensitive skin hardware

Ryu

2016

Robotica

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SUMMARYAs various robots are anticipated to coexist with humans in the near future, safe manipulation in unknown, cluttered environments becomes an important issue. Manipulation in an unknown environment, however, has been proven to be NP-Hard and the risk of unexpected human--robot collision hampers the dawning of the era of human--robot coexistence. We propose a non-contact-based sensitive skin as a means to provide safe manipulation hardware and interleaving planning between the workspace and the configuration space as software to solve manipulation problems in unknown, crowded environments. Novelty of the paper resides in demonstration of real time and yet complete path planning in an uncertain and crowded environment. To that end, we introduce the framework of the sensor-based interleaving planner (SBIP) whereby search completeness and safe manipulation are both guaranteed in cluttered environments. We study an interleaving mechanism between sensation in a workspace and execution in the corresponding configuration space for real-time planning in uncertain environments, thus the name interleaving planner implies.Applications of the proposed system include manipulators of a humanoid robot, surgical manipulators, and robotic manipulators working in hazardous and uncertain environments such as underwater, unexplored planets, and unstructured indoor spaces.

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Mobile manipulation in cluttered environments with humanoids: Integrated perception, task planning, and action execution

Cited by 23 publications

References 10 publications

Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

Autonomous robots for harsh environments: a holistic overview of current solutions and ongoing challenges

Safe manipulation in unknown, crowded environments via sensor-based interleaving planner: interleaving software and sensitive skin hardware

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